US County–Level Variation in Preterm Birth Rates, 2007-2019

Key Points Question What are the patterns and differences in preterm birth rates among US counties from 2007 to 2019? Findings In this cross-sectional study, small area estimation models were applied to birth registration records from the National Center for Health Statistics and demonstrated increases in preterm birth rates in 15.4% of counties between 2007 and 2019. The absolute difference between the 90th and 10th percentile counties was stable during the study period with a gap of 6.4% in 2007 and 6.1% in 2019. Meaning From 2007 to 2019, geographic disparities persisted, with increases in preterm birth rates in nearly 1 in 6 counties.


Small Area Models
We modeled   , the number of preterm or early preterm births in county i and age group k during year t from a total number of births (  ), using a Poisson distribution of the form   ~(  ,   ), where   is the proportion of births that were preterm.To model   , we assume (  )~(  +   ,   2 ), where (  ) =  (   1−  ),   is a random intercept for each group for each year with a vague normal distribution prior with mean zero and a variance of 100,   is a spatiotemporal random effect that incorporates correlation between age groups, and   2 is a variance parameter with a weakly informative gamma prior. 1 Our models account for correlation across time, space, and age group by modeling the spatiotemporal random effect (  ) using the multivariate space-time conditional autoregressive (MSTCAR) model 13 based on the multivariate CAR model of Gelfand and Vounatsou. 2Spatial correlation of the random effect for each county is defined by queen contiguity, which is a method of specifying the adjacency of counties commonly used in these types of analyses.Similarly, temporal correlation uses an approach similar to a standard autoregressive order 1 (AR(1)) model with a beta prior.Finally, correlations between age groups are estimated via an unstructured covariance matrix with an inverse Wishart prior. 1 We ran the MCMC algorithm with four chains for 6000 iterations, diagnosing convergence via trace plots for many model parameters and discarding the first 3000 iterations as burn-in.We generated estimates based on posterior medians, and 95% credible intervals were obtained by taking the 2.5-and 97.5-percentiles from the thinned post-burn-in samples.

Reliability of estimated rates
With these estimated rates, we then applied inclusion criteria to all 3,142 counties to ensure reporting of estimated reliable rates only for sufficiently large numbers of births and that, for each outcome, each maternal age group used a common set of counties for the entire study period.For a given maternal age group within a given county to be included in this analysis, we required that the estimated rates were statistically reliable (i.e., the credible interval width was less than the point estimate) and that the group-specific number of births was ≥100 for every year in the study period.For age-standardized rates, a given county's age-standardized rate was required to be statistically reliable and the total number of births was ≥100 for every year.*For a county to be included on these maps, the estimated rates were required to be reliable (i.e., the credible interval width was less than the point estimate) and the total number of births was ≥100 for every year in the study period.
eFigure 2. Age-Standardized Early Preterm Birth Rates in 2007 Among US States, Combined Statistical Areas, and Counties* *For a county to be included on these maps, the estimated rates were required to be reliable (i.e., the credible interval width was less than the point estimate) and the total number of births was ≥100 for every year in the study period.
eFigure 3. Age-Standardized Early Preterm Birth Rates in 2019 Among US States, Combined Statistical Areas, and Counties *For a county to be included on these maps, the estimated rates were required to be reliable (i.e., the credible interval width was less than the point estimate) and the total number of births was ≥100 for every year in the study period.For a county to be included on these maps, the estimated rates were required to be reliable (i.e., the credible interval width was less than the point estimate) and the total number of births was ≥100 for every year in the study period. eReferences

eFigure 4 .
Total Percent Change in Age-Standardized Early Preterm Birth Rates Between 2007-2019 among US States, Combined Statistical Areas, and Counties* *Mapped values are the point estimates of percent change calculated using log-linear regression from estimated rates.

eFigure 5 .
Annual Rate Ratios (95% CI) for Preterm Birth Rate and Early Preterm Birth Rate Stratified by Quartiles of the County-Level Social Vulnerability Index, 2007-2019* *We used the 2010 SVI for years 2007-2011, the 2014 SVI for years 2012-2015, the 2016 SVI for years 2016-2017, and the 2018 SVI for years 2018-2019 #The first quartile of SVI is the reference group.eFigure 6. Annual National and Distribution of County-Level Age-Standardized Preterm (A) and Early Preterm (B) Birth RatesThe bottom border, middle line, and the top border of the boxes represent the 25 th , 50 th , and 75 th percentiles, respectively, across all counties; whiskers the full range across counties (excluding outliers); and circles, the national rate

US Counties, ≤10 th percentile for early preterm birth rate (N=231) US Counties, ≥90 th percentile for early preterm birth rate (N=230)
Multivariate Regression Results for the Association of County-Level Socioeconomic Characteristics (Social Vulnerability Index) and Preterm Birth Rates in the US in 2019 County-Level Socioeconomic Characteristics (Social Vulnerability Index) of the 10 th Percentile and 90 th Percentile Counties for Early Preterm Birth Rate in the US in 2019 Distribution of Percent Change in Age-Standardized and Age-Specific National and County-Level Preterm and Early Preterm Birth Rates From 2007 to 2019 in the US Age-Specific Rates of Preterm and Early Preterm Birth at the National and County-Level in the United States, 2019 a Model 1 represents each individual theme in a separate regression model b Model 2 adjusts for SVI themes 1-2; c Model 3: SVI Themes 1-3; d Model 4: SVI Themes 1-4; eTable 2.Positive percent change represents increase and negative percent change represents decrease in age-standardized rates of preterm and early preterm birth eTable 4.

per 100 live births) US County-Level Age-Specific Rates (per 100 live births)
National Age-Standardized Rates of Preterm Birth Outcomes and Distribution of Age-Standardized Rates of Preterm Birth Among US Counties in 2007 and 2019 among Individuals with a Singleton First Live Birth

per 100 live births) US County-Level Age-Standardized Rates (per 100 live births)
Age-Standardized Preterm Birth Rates in 2007 Among US States, Combined Statistical Areas, and Counties*